Abstract

Multi-disciplinary engineering environments, e.g., in automation systems engineering, typically involve different stakeholder groups and engineering disciplines using a variety of specific tools and data models. Defects in individual disciplines can have a major impact on product and process quality in terms of additional cost and effort for defect repair and can lead to project delays. Early defects detection and avoidance in future projects are key challenges for project and quality managers to improve the product and process quality. In this paper we present an adaptation of the defect causal analysis (DCA) approach, which has been found effective and efficient to improve product quality in software engineering contexts. Applying DCA in multi-disciplinary engineering environments enables a systematic analysis of defects and candidate root causes, and can help providing countermeasures for product and process quality. The feasibility study of the adapted DCA has shown that the adaptation is useful and enables improving defect detection and prevention in multi-disciplinary engineering projects and fosters engineering process improvement.